Improving accuracy of cavitation severity detection in centrifugal pumps using a hybrid feature selection technique

Abstract Although the severity of cavitation determines the type of maintenance procedure, most of the previous studies have been focused only on the detection. This paper presents a system for detection of cavitation severity in centrifugal pumps and improving its accuracy using a hybrid feature selection technique. The vibration data used in this research is acquired from a model pump. The vibrations of the pump’s outlet is measured in three different pump conditions including no cavitation, limited cavitation and developed cavitation. Then, empirical mode decomposition (EMD) method is used to decompose original signals into a number of intrinsic mode functions (IMFs). After extracting the IMFs, several statistical features are extracted from the first six IMFs. After that, a generalized regression neural network (GRNN) is used for fault classification. Correct classification rate of GRNN using all the extracted features as an input vector is 97.5%. A ten-fold cross-validation is conducted to evaluate the data. In order to increase the classification accuracy and eliminate redundant features, a hybrid feature selection algorithm is proposed. A comparison is also made between the results of radial basis function and multi-layer perceptron networks, as well. By using the selected features, not only the number of features is reduced, but also the classification accuracy is increased to 100% for all the three mentioned artificial neural networks. The selected features also determine the best IMFs that can be used in diagnosis of cavitation.

[1]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[2]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  I. R. Praveen Krishna,et al.  Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis , 2014 .

[5]  P. Burman.  A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods , 1989 .

[6]  Kang Zhang,et al.  An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems , 2012 .

[7]  Minqiang Xu,et al.  An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis , 2015 .

[8]  Saeid Farokhzad,et al.  Acoustic Based Cavitation Detection of Centrifugal Pump by Neural Network , 2013 .

[9]  J. Rafiee,et al.  INTELLIGENT CONDITION MONITORING OF A GEARBOX USING ARTIFICIAL NEURAL NETWORK , 2007 .

[10]  Stefan Holban,et al.  A Computational Intelligence Approach for Ranking Risk Factors in Preterm Birth , 2007, 2007 4th International Symposium on Applied Computational Intelligence and Informatics.

[11]  Jun Yang,et al.  Fluid cavitation detection method with phase demodulation of ultrasonic signal , 2015 .

[12]  P. K. Kankar,et al.  A comparison of feature ranking techniques for fault diagnosis of ball bearing , 2016, Soft Comput..

[13]  Duc Truong Pham,et al.  Intelligent production machines and systems - 2nd I*PROMS virtual international conference 3-14 July 2006 , 2006 .

[14]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[15]  N. R. Sakthivel,et al.  Vibration based fault diagnosis of monoblock centrifugal pump using decision tree , 2010, Expert Syst. Appl..

[16]  Xiaofeng Liu,et al.  Bearing faults diagnostics based on hybrid LS-SVM and EMD method , 2015 .

[17]  Fengshou Gu,et al.  Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network , 2011 .

[18]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[19]  Mohammad Hassan Moradi,et al.  Design and implementation of an automatic condition‐monitoring expert system for ball‐bearing fault detection , 2008 .

[20]  Zeinab Salehahmadi,et al.  How Can Bee Colony Algorithm Serve Medicine? , 2014, World journal of plastic surgery.

[21]  Zhengjia He,et al.  A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM , 2012 .

[22]  M. Čdina.  DETECTION OF CAVITATION PHENOMENON IN A CENTRIFUGAL PUMP USING AUDIBLE SOUND , 2003 .

[23]  V. Sugumaran,et al.  Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump , 2013 .

[24]  M. Salman Leong,et al.  An experimental study of cavitation detection in a centrifugal pump using envelope analysis , 2008 .

[25]  Jurij Prezelj,et al.  Detection of cavitation in operation of kinetic pumps. Use of discrete frequency tone in audible spectra , 2009 .

[26]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .

[27]  Ahmad Ghasemloonia,et al.  Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis , 2011, Expert Syst. Appl..

[28]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[29]  H. Q. Wang,et al.  Fault Diagnosis of Centrifugal Pump Using Symptom Parameters in Frequency Domain , 2007 .

[30]  Ian Howard,et al.  A vibration cavitation sensitivity parameter based on spectral and statistical methods , 2015, Expert Syst. Appl..